Behaviorally Adaptive Multi-Robot Hazard Localization in Failure-Prone, Communication-Denied Environments

2025-08-06

AI Summary

This paper addresses multi-robot hazardous mapping in high-risk, communication-constrained, and failure-prone environments—such as post-disaster rubble fields and underground mines. We propose the Behavior-Adaptive Planning Framework (BAPP), whose core innovation lies in introducing a behavior entropy metric and a tunable risk-sensitivity parameter to dynamically optimize information-gathering strategies. Two specialized algorithms are developed: BAPP-TID (Triggered High-Fidelity Sensing) and BAPP-SIG (Safety-Critical High-Risk Deployment). BAPP integrates distributed path planning, spatial partitioning, mobile base station repositioning, and role-aware heterogeneous coordination. Simulation results demonstrate that BAPP significantly outperforms Shannon entropy-based baselines in both entropy decay rate and robot survival rate, exhibits strong scalability, and substantially enhances risk-sensitive exploration efficacy and system robustness in complex environments.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Multiagent Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

We address the challenge of multi-robot autonomous hazard mapping in high-risk, failure-prone, communication-denied environments such as post-disaster zones, underground mines, caves, and planetary surfaces. In these missions, robots must explore and map hazards while minimizing the risk of failure due to environmental threats or hardware limitations. We introduce a behavior-adaptive, information-theoretic planning framework for multi-robot teams grounded in the concept of Behavioral Entropy (BE), that generalizes Shannon entropy (SE) to capture diverse human-like uncertainty evaluations. Building on this formulation, we propose the Behavior-Adaptive Path Planning (BAPP) framework, which modulates information gathering strategies via a tunable risk-sensitivity parameter, and present two planning algorithms: BAPP-TID for intelligent triggering of high-fidelity robots, and BAPP-SIG for safe deployment under high risk. We provide theoretical insights on the informativeness of the proposed BAPP framework and validate its effectiveness through both single-robot and multi-robot simulations. Our results show that the BAPP stack consistently outperforms Shannon-based and random strategies: BAPP-TID accelerates entropy reduction, while BAPP-SIG improves robot survivability with minimal loss in information gain. In multi-agent deployments, BAPP scales effectively through spatial partitioning, mobile base relocation, and role-aware heterogeneity. These findings underscore the value of behavior-adaptive planning for robust, risk-sensitive exploration in complex, failure-prone environments.

Problem

• Multi-robot hazard mapping in high-risk, communication-denied environments
• Minimizing robot failure risks during exploration and mapping
• Adapting information gathering strategies based on risk sensitivity

Innovation

• Behavior-adaptive planning with risk sensitivity
• BAPP-TID for intelligent robot triggering
• BAPP-SIG for safe high-risk deployment